Psychosis Project - Regression and ANOVA tables
T. Bryan Jackson
Load Libraries
library("ggplot2")
library("brms")
library("caret")
library("jtools")
library("multcomp")
library("emmeans")
library("apaTables")
library("papaja")Prepare Dataset
Set Up Group Factor, Split Dataset, etc.
Load Dataset
setwd("~/Desktop/PsychosisProject/Analyses")
raw_data <- read.csv("~/Desktop/PsychosisProject/Analyses/Data.csv")
dataset = raw_data
dataset$Group <- as.factor(dataset$Group)
dataset$Group <- factor(dataset$Group, levels=c('HC', 'CHR', 'ECP', 'CP'))
set.seed(4)
dataset_4 = split(dataset,dataset$Group)
CP = dataset_4$CP
ECP = dataset_4$ECP
CHR = dataset_4$CHR
HC = dataset_4$HC
noHC = rbind(CHR, ECP, CP)Descriptive Stats
summary(dataset)## Subj Dataset Alcohol Marj
## Length:448 Length:448 Min. :0.000 Min. :0.0000
## Class :character Class :character 1st Qu.:0.000 1st Qu.:0.0000
## Mode :character Mode :character Median :1.000 Median :1.0000
## Mean :0.877 Mean :0.8993
## 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :3.000 Max. :3.0000
## NA's :1 NA's :1
## Age SIPS.P SIPS.N PANSS.P
## Min. :12.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.:19.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median :22.00 Median : 5.000 Median : 2.000 Median :11.00
## Mean :26.62 Mean : 6.655 Mean : 4.696 Mean :10.22
## 3rd Qu.:29.00 3rd Qu.:12.500 3rd Qu.: 8.000 3rd Qu.:16.00
## Max. :65.00 Max. :21.000 Max. :23.000 Max. :29.00
## NA's :10 NA's :277 NA's :277 NA's :172
## PANSS.N PANSS.Cog PANSS_Total_score CCBN
## Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. :0.3061
## 1st Qu.: 0.000 1st Qu.: 9.0 1st Qu.:40.00 1st Qu.:0.6727
## Median :10.000 Median :10.0 Median :48.50 Median :0.7247
## Mean : 9.616 Mean :10.6 Mean :46.98 Mean :0.7151
## 3rd Qu.:15.000 3rd Qu.:13.0 3rd Qu.:57.00 3rd Qu.:0.7803
## Max. :29.000 Max. :18.0 Max. :78.00 Max. :0.9470
## NA's :172 NA's :325 NA's :326
## MCBN FPN CON DMN
## Min. :0.1607 Min. :0.3864 Min. :0.2857 Min. :0.3788
## 1st Qu.:0.5893 1st Qu.:0.6481 1st Qu.:0.6429 1st Qu.:0.6812
## Median :0.7009 Median :0.7000 Median :0.7540 Median :0.7355
## Mean :0.6760 Mean :0.6952 Mean :0.7287 Mean :0.7315
## 3rd Qu.:0.7812 3rd Qu.:0.7462 3rd Qu.:0.8333 3rd Qu.:0.7910
## Max. :0.9821 Max. :0.9364 Max. :1.0000 Max. :0.9545
##
## EN MN VN AN
## Min. :0.2799 Min. :0.4611 Min. :0.6384 Min. :0.4859
## 1st Qu.:0.6067 1st Qu.:0.7298 1st Qu.:0.7660 1st Qu.:0.7158
## Median :0.6538 Median :0.7762 Median :0.8012 Median :0.7692
## Mean :0.6447 Mean :0.7769 Mean :0.8047 Mean :0.7654
## 3rd Qu.:0.6923 3rd Qu.:0.8286 3rd Qu.:0.8449 3rd Qu.:0.8205
## Max. :0.8205 Max. :0.9810 Max. :0.9536 Max. :0.9615
## NA's :1
## CPZ_eqiuv CBBG Group
## Min. : 0.0 Min. :0.5031 HC :204
## 1st Qu.: 0.0 1st Qu.:0.6652 CHR: 91
## Median : 0.0 Median :0.7000 ECP:104
## Mean : 221.9 Mean :0.6978 CP : 49
## 3rd Qu.: 150.0 3rd Qu.:0.7336
## Max. :7600.0 Max. :0.8605
## HC
summary(HC)## Subj Dataset Alcohol Marj
## Length:204 Length:204 Min. :0.0000 Min. :0.0000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:0.0000
## Mode :character Mode :character Median :1.0000 Median :1.0000
## Mean :0.7931 Mean :0.8079
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :3.0000 Max. :3.0000
## NA's :1 NA's :1
## Age SIPS.P SIPS.N PANSS.P
## Min. :12.00 Min. :0.00 Min. :0.0000 Min. : 0.000
## 1st Qu.:19.00 1st Qu.:0.00 1st Qu.:0.0000 1st Qu.: 0.000
## Median :22.38 Median :0.00 Median :0.0000 Median : 0.000
## Mean :27.72 Mean :0.60 Mean :0.4375 Mean : 4.163
## 3rd Qu.:34.25 3rd Qu.:0.25 3rd Qu.:1.0000 3rd Qu.: 9.000
## Max. :65.00 Max. :5.00 Max. :3.0000 Max. :25.000
## NA's :8 NA's :124 NA's :124 NA's :81
## PANSS.N PANSS.Cog PANSS_Total_score CCBN
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. :0.3061
## 1st Qu.: 0.000 1st Qu.: 9.00 1st Qu.:36.00 1st Qu.:0.6837
## Median : 0.000 Median : 9.00 Median :46.00 Median :0.7361
## Mean : 3.675 Mean : 9.07 Mean :41.53 Mean :0.7266
## 3rd Qu.: 7.000 3rd Qu.:10.00 3rd Qu.:53.50 3rd Qu.:0.7854
## Max. :23.000 Max. :16.00 Max. :78.00 Max. :0.9015
## NA's :81 NA's :161 NA's :161
## MCBN FPN CON DMN
## Min. :0.1607 Min. :0.3864 Min. :0.2857 Min. :0.5013
## 1st Qu.:0.6119 1st Qu.:0.6538 1st Qu.:0.6667 1st Qu.:0.6856
## Median :0.7143 Median :0.7030 Median :0.7778 Median :0.7538
## Mean :0.6915 Mean :0.6971 Mean :0.7473 Mean :0.7398
## 3rd Qu.:0.8036 3rd Qu.:0.7485 3rd Qu.:0.8512 3rd Qu.:0.8005
## Max. :0.9821 Max. :0.9000 Max. :1.0000 Max. :0.9545
##
## EN MN VN AN
## Min. :0.4509 Min. :0.5183 Min. :0.6771 Min. :0.5365
## 1st Qu.:0.6152 1st Qu.:0.7355 1st Qu.:0.7681 1st Qu.:0.7265
## Median :0.6608 Median :0.7825 Median :0.8034 Median :0.7842
## Mean :0.6550 Mean :0.7809 Mean :0.8061 Mean :0.7795
## 3rd Qu.:0.7014 3rd Qu.:0.8230 3rd Qu.:0.8435 3rd Qu.:0.8280
## Max. :0.7756 Max. :0.9810 Max. :0.9526 Max. :0.9615
##
## CPZ_eqiuv CBBG Group
## Min. :0 Min. :0.5031 HC :204
## 1st Qu.:0 1st Qu.:0.6711 CHR: 0
## Median :0 Median :0.7096 ECP: 0
## Mean :0 Mean :0.7066 CP : 0
## 3rd Qu.:0 3rd Qu.:0.7400
## Max. :0 Max. :0.8605
## sd(HC$Age, na.rm=TRUE)## [1] 12.4743sd(HC$Alcohol, na.rm=TRUE)## [1] 0.7938824sd(HC$Marj, na.rm=TRUE)## [1] 0.8660465sd(HC$CPZ_eqiuv, na.rm=TRUE)## [1] 0sd(HC$PANSS.P, na.rm=TRUE)## [1] 6.420222sd(HC$PANSS.N, na.rm=TRUE)## [1] 5.940271sd(HC$PANSS.Cog, na.rm=TRUE)## [1] 2.676132sd(HC$SIPS.P, na.rm=TRUE)## [1] 1.268908sd(HC$SIPS.N, na.rm=TRUE)## [1] 0.7604754CHR
summary(CHR)## Subj Dataset Alcohol Marj
## Length:91 Length:91 Min. :0.0000 Min. :0.0000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:0.0000
## Mode :character Mode :character Median :0.0000 Median :0.0000
## Mean :0.3956 Mean :0.1319
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
##
## Age SIPS.P SIPS.N PANSS.P PANSS.N
## Min. :13.00 Min. : 0.00 Min. : 0.00 Min. : NA Min. : NA
## 1st Qu.:18.50 1st Qu.: 9.00 1st Qu.: 3.00 1st Qu.: NA 1st Qu.: NA
## Median :19.00 Median :12.00 Median : 7.00 Median : NA Median : NA
## Mean :19.51 Mean :11.98 Mean : 8.44 Mean :NaN Mean :NaN
## 3rd Qu.:21.00 3rd Qu.:15.00 3rd Qu.:13.00 3rd Qu.: NA 3rd Qu.: NA
## Max. :24.00 Max. :21.00 Max. :23.00 Max. : NA Max. : NA
## NA's :91 NA's :91
## PANSS.Cog PANSS_Total_score CCBN MCBN
## Min. : NA Min. : NA Min. :0.3561 Min. :0.2649
## 1st Qu.: NA 1st Qu.: NA 1st Qu.:0.6290 1st Qu.:0.5920
## Median : NA Median : NA Median :0.7146 Median :0.7024
## Mean :NaN Mean :NaN Mean :0.6936 Mean :0.6886
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:0.7790 3rd Qu.:0.8274
## Max. : NA Max. : NA Max. :0.8636 Max. :0.9821
## NA's :91 NA's :91
## FPN CON DMN EN
## Min. :0.3924 Min. :0.2857 Min. :0.4545 Min. :0.2799
## 1st Qu.:0.6571 1st Qu.:0.6290 1st Qu.:0.6982 1st Qu.:0.5790
## Median :0.7000 Median :0.7222 Median :0.7475 Median :0.6357
## Mean :0.7061 Mean :0.7011 Mean :0.7456 Mean :0.6216
## 3rd Qu.:0.7576 3rd Qu.:0.8214 3rd Qu.:0.7942 3rd Qu.:0.6784
## Max. :0.8818 Max. :1.0000 Max. :0.9167 Max. :0.7821
##
## MN VN AN CPZ_eqiuv
## Min. :0.6294 Min. :0.6781 Min. :0.4861 Min. :0
## 1st Qu.:0.7194 1st Qu.:0.7636 1st Qu.:0.7131 1st Qu.:0
## Median :0.7786 Median :0.8155 Median :0.7735 Median :0
## Mean :0.7807 Mean :0.8137 Mean :0.7641 Mean :0
## 3rd Qu.:0.8333 3rd Qu.:0.8668 3rd Qu.:0.8205 3rd Qu.:0
## Max. :0.9619 Max. :0.9536 Max. :0.9359 Max. :0
## NA's :1
## CBBG Group
## Min. :0.5281 HC : 0
## 1st Qu.:0.6442 CHR:91
## Median :0.6886 ECP: 0
## Mean :0.6815 CP : 0
## 3rd Qu.:0.7274
## Max. :0.8316
## sd(CHR$Age, na.rm=TRUE)## [1] 2.115412sd(CHR$Alcohol, na.rm=TRUE)## [1] 0.4916892sd(CHR$Marj, na.rm=TRUE)## [1] 0.3402219sd(CHR$CPZ_eqiuv, na.rm=TRUE)## [1] 0sd(CHR$PANSS.P, na.rm=TRUE)## [1] NAsd(CHR$PANSS.N, na.rm=TRUE)## [1] NAsd(CHR$PANSS.Cog, na.rm=TRUE)## [1] NAsd(CHR$SIPS.P, na.rm=TRUE)## [1] 4.464621sd(CHR$SIPS.N, na.rm=TRUE)## [1] 6.347718ECP
summary(ECP)## Subj Dataset Alcohol Marj
## Length:104 Length:104 Min. :1.000 Min. :1.000
## Class :character Class :character 1st Qu.:1.000 1st Qu.:1.000
## Mode :character Mode :character Median :1.000 Median :1.000
## Mean :1.231 Mean :1.529
## 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :3.000 Max. :3.000
##
## Age SIPS.P SIPS.N PANSS.P PANSS.N
## Min. :17.92 Min. : NA Min. : NA Min. : 0.00 Min. : 0.00
## 1st Qu.:20.10 1st Qu.: NA 1st Qu.: NA 1st Qu.:12.00 1st Qu.: 9.00
## Median :22.00 Median : NA Median : NA Median :15.00 Median :14.00
## Mean :22.71 Mean :NaN Mean :NaN Mean :15.07 Mean :14.11
## 3rd Qu.:24.17 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:18.25 3rd Qu.:18.00
## Max. :34.08 Max. : NA Max. : NA Max. :29.00 Max. :29.00
## NA's :2 NA's :104 NA's :104
## PANSS.Cog PANSS_Total_score CCBN MCBN
## Min. : 0.00 Min. : 0.00 Min. :0.4235 Min. :0.2292
## 1st Qu.: 9.00 1st Qu.:42.00 1st Qu.:0.6739 1st Qu.:0.5482
## Median :11.00 Median :51.00 Median :0.7121 Median :0.6637
## Mean :11.43 Mean :49.95 Mean :0.7138 Mean :0.6340
## 3rd Qu.:14.00 3rd Qu.:60.00 3rd Qu.:0.7626 3rd Qu.:0.7359
## Max. :18.00 Max. :77.00 Max. :0.9470 Max. :0.9821
## NA's :24 NA's :25
## FPN CON DMN EN
## Min. :0.4152 Min. :0.2857 Min. :0.3788 Min. :0.3440
## 1st Qu.:0.6273 1st Qu.:0.5815 1st Qu.:0.6509 1st Qu.:0.6035
## Median :0.6902 Median :0.7381 Median :0.7172 Median :0.6407
## Mean :0.6825 Mean :0.7016 Mean :0.7052 Mean :0.6373
## 3rd Qu.:0.7424 3rd Qu.:0.8115 3rd Qu.:0.7626 3rd Qu.:0.6830
## Max. :0.9364 Max. :1.0000 Max. :0.9545 Max. :0.8205
##
## MN VN AN CPZ_eqiuv
## Min. :0.4611 Min. :0.6384 Min. :0.4859 Min. : 0.0
## 1st Qu.:0.7079 1st Qu.:0.7704 1st Qu.:0.6856 1st Qu.: 0.0
## Median :0.7579 Median :0.7982 Median :0.7489 Median : 300.0
## Mean :0.7629 Mean :0.8047 Mean :0.7467 Mean : 411.2
## 3rd Qu.:0.8321 3rd Qu.:0.8343 3rd Qu.:0.8077 3rd Qu.: 400.0
## Max. :0.9286 Max. :0.9315 Max. :0.9359 Max. :4797.0
##
## CBBG Group
## Min. :0.5219 HC : 0
## 1st Qu.:0.6592 CHR: 0
## Median :0.6895 ECP:104
## Mean :0.6874 CP : 0
## 3rd Qu.:0.7195
## Max. :0.8228
## sd(ECP$Age, na.rm=TRUE)## [1] 3.438597sd(ECP$Alcohol, na.rm=TRUE)## [1] 0.6267619sd(ECP$Marj, na.rm=TRUE)## [1] 0.858501sd(ECP$CPZ_eqiuv, na.rm=TRUE)## [1] 648.8351sd(ECP$PANSS.P, na.rm=TRUE)## [1] 5.120423sd(ECP$PANSS.N, na.rm=TRUE)## [1] 5.923326sd(ECP$PANSS.Cog, na.rm=TRUE)## [1] 3.279298sd(ECP$SIPS.P, na.rm=TRUE)## [1] NAsd(ECP$SIPS.N, na.rm=TRUE)## [1] NACP
summary(CP)## Subj Dataset Alcohol Marj
## Length:49 Length:49 Min. :1.000 Min. :1.000
## Class :character Class :character 1st Qu.:1.000 1st Qu.:1.000
## Mode :character Mode :character Median :1.000 Median :1.000
## Mean :1.367 Mean :1.367
## 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :3.000 Max. :3.000
##
## Age SIPS.P SIPS.N PANSS.P PANSS.N
## Min. :19.00 Min. : NA Min. : NA Min. : 7.00 Min. : 7
## 1st Qu.:36.00 1st Qu.: NA 1st Qu.: NA 1st Qu.:13.00 1st Qu.:11
## Median :44.00 Median : NA Median : NA Median :15.00 Median :14
## Mean :43.57 Mean :NaN Mean :NaN Mean :15.14 Mean :15
## 3rd Qu.:52.00 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:18.00 3rd Qu.:18
## Max. :64.00 Max. : NA Max. : NA Max. :25.00 Max. :29
## NA's :49 NA's :49
## PANSS.Cog PANSS_Total_score CCBN MCBN
## Min. : NA Min. : NA Min. :0.4545 Min. :0.3036
## 1st Qu.: NA 1st Qu.: NA 1st Qu.:0.6591 1st Qu.:0.6381
## Median : NA Median : NA Median :0.7273 Median :0.7143
## Mean :NaN Mean :NaN Mean :0.7101 Mean :0.6769
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:0.7727 3rd Qu.:0.7738
## Max. : NA Max. : NA Max. :0.8409 Max. :0.9643
## NA's :49 NA's :49
## FPN CON DMN EN
## Min. :0.4576 Min. :0.3651 Min. :0.5066 Min. :0.4676
## 1st Qu.:0.6394 1st Qu.:0.6905 1st Qu.:0.6894 1st Qu.:0.6346
## Median :0.7000 Median :0.7778 Median :0.7285 Median :0.6667
## Mean :0.6947 Mean :0.7603 Mean :0.7264 Mean :0.6605
## 3rd Qu.:0.7364 3rd Qu.:0.8492 3rd Qu.:0.7727 3rd Qu.:0.6902
## Max. :0.8545 Max. :1.0000 Max. :0.8636 Max. :0.7714
##
## MN VN AN CPZ_eqiuv
## Min. :0.6484 Min. :0.6808 Min. :0.5107 Min. : 0
## 1st Qu.:0.7571 1st Qu.:0.7446 1st Qu.:0.7179 1st Qu.: 268
## Median :0.7794 Median :0.7776 Median :0.7564 Median : 600
## Mean :0.7830 Mean :0.7819 Mean :0.7488 Mean :1156
## 3rd Qu.:0.8286 3rd Qu.:0.8246 3rd Qu.:0.7863 3rd Qu.:1600
## Max. :0.8857 Max. :0.9224 Max. :0.9231 Max. :7600
##
## CBBG Group
## Min. :0.5426 HC : 0
## 1st Qu.:0.6851 CHR: 0
## Median :0.7096 ECP: 0
## Mean :0.7133 CP :49
## 3rd Qu.:0.7430
## Max. :0.7921
## sd(CP$Age, na.rm=TRUE)## [1] 10.45028sd(CP$Alcohol, na.rm=TRUE)## [1] 0.6675165sd(CP$Marj, na.rm=TRUE)## [1] 0.6980293sd(CP$CPZ_eqiuv, na.rm=TRUE)## [1] 1399.323sd(CP$PANSS.P, na.rm=TRUE)## [1] 4.067145sd(CP$PANSS.N, na.rm=TRUE)## [1] 5.303301sd(CP$PANSS.Cog, na.rm=TRUE)## [1] NAsd(CP$SIPS.P, na.rm=TRUE)## [1] NAsd(CP$SIPS.N, na.rm=TRUE)## [1] NAGroup Differences in Symptoms
PANSS-P
GroupDiffPP <- aov(PANSS.P ~ Group + Alcohol+ Marj + Age + CPZ_eqiuv, dataset)
summary(GroupDiffPP)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 8589 4294 174.617 < 2e-16 ***
## Alcohol 1 49 49 1.987 0.159820
## Marj 1 326 326 13.239 0.000331 ***
## Age 1 832 832 33.847 1.75e-08 ***
## CPZ_eqiuv 1 2 2 0.072 0.788757
## Residuals 260 6394 25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 181 observations deleted due to missingnessPANSS_P_group_plot <- effect_plot(GroupDiffPP, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
PANSS_P_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank(), axis.title.y = element_blank())
posthoc_PP <- glht(GroupDiffPP, linfct = mcp(Group="Tukey"))
summary(posthoc_PP, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = PANSS.P ~ Group + Alcohol + Marj + Age + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## ECP - HC == 0 9.137 0.782 11.684 < 2e-16 ***
## CP - HC == 0 12.990 1.026 12.657 < 2e-16 ***
## CP - ECP == 0 3.853 1.133 3.401 0.000776 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)PANSS-N
GroupDiffPN <- aov(PANSS.N ~ Group + Alcohol+ Marj + Age + CPZ_eqiuv, dataset)
summary(GroupDiffPN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7852 3926 130.054 < 2e-16 ***
## Alcohol 1 2 2 0.065 0.79929
## Marj 1 252 252 8.345 0.00419 **
## Age 1 864 864 28.620 1.93e-07 ***
## CPZ_eqiuv 1 4 4 0.125 0.72426
## Residuals 260 7849 30
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 181 observations deleted due to missingnessPANSS_N_group_plot <- effect_plot(GroupDiffPN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
PANSS_N_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank(), axis.title.y = element_blank())
posthoc_PN <- glht(GroupDiffPN, linfct = mcp(Group="Tukey"))
summary(posthoc_PN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = PANSS.N ~ Group + Alcohol + Marj + Age + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## ECP - HC == 0 8.2279 0.8664 9.497 < 2e-16 ***
## CP - HC == 0 13.1886 1.1370 11.600 < 2e-16 ***
## CP - ECP == 0 4.9607 1.2551 3.953 9.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)PANSS-Cog
GroupDiffPC <- aov(PANSS.Cog ~ Group + Alcohol+ Marj + Age + CPZ_eqiuv, dataset)
summary(GroupDiffPC)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 113.4 113.36 14.763 0.000206 ***
## Alcohol 1 0.1 0.06 0.008 0.928996
## Marj 1 0.6 0.56 0.072 0.788248
## Age 1 4.4 4.38 0.571 0.451695
## CPZ_eqiuv 1 83.4 83.44 10.866 0.001326 **
## Residuals 108 829.3 7.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 334 observations deleted due to missingnessPANSS_Cog_group_plot <- effect_plot(GroupDiffPC, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
PANSS_Cog_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank(), axis.title.y = element_blank())
SIPS-P
GroupDiffSP <- aov(SIPS.P ~ Group + Alcohol + Marj + Age , dataset)
summary(GroupDiffSP)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 5511 5511 515.689 < 2e-16 ***
## Alcohol 1 3 3 0.306 0.58101
## Marj 1 108 108 10.110 0.00176 **
## Age 1 36 36 3.340 0.06943 .
## Residuals 166 1774 11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 277 observations deleted due to missingnessSIPS_P_group_plot <- effect_plot(GroupDiffSP, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
SIPS_P_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank(), axis.title.y = element_blank())
posthoc_SP <- glht(GroupDiffSP, linfct = mcp(Group="Tukey"))
summary(posthoc_SP, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = SIPS.P ~ Group + Alcohol + Marj + Age, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 10.6776 0.5599 19.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)SIPS-N
GroupDiffSN <- aov(SIPS.N ~ Group + Alcohol+ Marj + Age, dataset)
summary(GroupDiffSN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 2726 2726.1 131.540 < 2e-16 ***
## Alcohol 1 32 32.2 1.555 0.21421
## Marj 1 199 199.3 9.618 0.00226 **
## Age 1 0 0.3 0.016 0.90054
## Residuals 166 3440 20.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 277 observations deleted due to missingnessSIPS_N_group_plot <- effect_plot(GroupDiffSN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
SIPS_N_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank(), axis.title.y = element_blank())
posthoc_SN <- glht(GroupDiffSN, linfct = mcp(Group="Tukey"))
summary(posthoc_SN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = SIPS.N ~ Group + Alcohol + Marj + Age, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 7.0730 0.7796 9.072 4.44e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)Group Differences in CB-BG GE
CCBN
GroupDiffC <- aov(CCBN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffC)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.072 0.02416 2.913 0.034138 *
## Alcohol 1 0.006 0.00569 0.686 0.407850
## Marj 1 0.042 0.04161 5.016 0.025622 *
## Age 1 0.009 0.00948 1.143 0.285676
## Dataset 2 0.142 0.07093 8.552 0.000228 ***
## CPZ_eqiuv 1 0.010 0.00988 1.191 0.275804
## Residuals 428 3.550 0.00829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessCCBN_group_plot <- effect_plot(GroupDiffC, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
CCBN_group_plot + ylab("CCBN GE") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_C <- glht(GroupDiffC, linfct = mcp(Group="Tukey"))
summary(posthoc_C, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = CCBN ~ Group + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.03594 0.01417 -2.537 0.0346 *
## ECP - HC == 0 0.01993 0.01511 1.318 0.2821
## CP - HC == 0 -0.01912 0.01882 -1.016 0.3724
## ECP - CHR == 0 0.05587 0.02090 2.673 0.0346 *
## CP - CHR == 0 0.01682 0.02342 0.718 0.4730
## CP - ECP == 0 -0.03905 0.02143 -1.822 0.1382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)MCBN
GroupDiffM <- aov(MCBN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffM)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.257 0.08575 3.972 0.00823 **
## Alcohol 1 0.002 0.00200 0.093 0.76112
## Marj 1 0.052 0.05180 2.399 0.12214
## Age 1 0.078 0.07830 3.626 0.05755 .
## Dataset 2 0.531 0.26529 12.287 6.48e-06 ***
## CPZ_eqiuv 1 0.050 0.05038 2.333 0.12737
## Residuals 428 9.241 0.02159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessMCBN_group_plot <- effect_plot(GroupDiffM, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
MCBN_group_plot + ylab("MCBN GE") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_M <- glht(GroupDiffM, linfct = mcp(Group="Tukey"))
summary(posthoc_M, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = MCBN ~ Group + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.0009782 0.0228613 -0.043 0.9659
## ECP - HC == 0 -0.0193331 0.0243869 -0.793 0.6425
## CP - HC == 0 -0.0749526 0.0303706 -2.468 0.0839 .
## ECP - CHR == 0 -0.0183550 0.0337260 -0.544 0.7039
## CP - CHR == 0 -0.0739744 0.0377878 -1.958 0.1528
## CP - ECP == 0 -0.0556194 0.0345726 -1.609 0.2168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- fdr method)Group Differences in Cort GE
FPN
GroupDiffFPN <- aov(FPN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffFPN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.0251 0.008358 1.175 0.319
## Alcohol 1 0.0106 0.010565 1.486 0.224
## Marj 1 0.0104 0.010354 1.456 0.228
## Age 1 0.0112 0.011224 1.578 0.210
## Dataset 2 0.0196 0.009815 1.380 0.253
## CPZ_eqiuv 1 0.0016 0.001613 0.227 0.634
## Residuals 428 3.0438 0.007112
## 10 observations deleted due to missingnessFPN_group_plot <- effect_plot(GroupDiffFPN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
FPN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_FPN <- glht(GroupDiffFPN, linfct = mcp(Group="Tukey"))
summary(posthoc_FPN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = FPN ~ Group + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 0.004362 0.013120 0.332 0.888
## ECP - HC == 0 -0.008782 0.013996 -0.628 0.888
## CP - HC == 0 0.007336 0.017430 0.421 0.888
## ECP - CHR == 0 -0.013145 0.019355 -0.679 0.888
## CP - CHR == 0 0.002974 0.021686 0.137 0.891
## CP - ECP == 0 0.016118 0.019841 0.812 0.888
## (Adjusted p values reported -- fdr method)CON
GroupDiffCON <- aov(CON ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffCON)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.256 0.08543 3.813 0.0102 *
## Alcohol 1 0.015 0.01527 0.681 0.4095
## Marj 1 0.003 0.00306 0.137 0.7119
## Age 1 0.000 0.00000 0.000 0.9945
## Dataset 2 0.096 0.04793 2.139 0.1190
## CPZ_eqiuv 1 0.000 0.00000 0.000 0.9951
## Residuals 428 9.590 0.02241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessCON_group_plot <- effect_plot(GroupDiffCON, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
CON_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_CON <- glht(GroupDiffCON, linfct = mcp(Group="Tukey"))
summary(posthoc_CON, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = CON ~ Group + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.050939 0.023289 -2.187 0.176
## ECP - HC == 0 -0.025262 0.024843 -1.017 0.491
## CP - HC == 0 0.009255 0.030938 0.299 0.765
## ECP - CHR == 0 0.025677 0.034356 0.747 0.546
## CP - CHR == 0 0.060194 0.038494 1.564 0.356
## CP - ECP == 0 0.034517 0.035219 0.980 0.491
## (Adjusted p values reported -- fdr method)DMN
GroupDiffDMN <- aov(DMN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffDMN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.1076 0.03586 4.898 0.00234 **
## Alcohol 1 0.0001 0.00008 0.011 0.91671
## Marj 1 0.0028 0.00279 0.381 0.53748
## Age 1 0.0065 0.00654 0.894 0.34498
## Dataset 2 0.0243 0.01216 1.661 0.19124
## CPZ_eqiuv 1 0.0040 0.00404 0.552 0.45778
## Residuals 428 3.1331 0.00732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessDMN_group_plot <- effect_plot(GroupDiffDMN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
DMN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_DMN <- glht(GroupDiffDMN, linfct = mcp(Group="Tukey"))
summary(posthoc_DMN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = DMN ~ Group + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.002882 0.013311 -0.216 0.869
## ECP - HC == 0 -0.025542 0.014200 -1.799 0.437
## CP - HC == 0 -0.006509 0.017684 -0.368 0.869
## ECP - CHR == 0 -0.022661 0.019637 -1.154 0.690
## CP - CHR == 0 -0.003628 0.022002 -0.165 0.869
## CP - ECP == 0 0.019033 0.020130 0.945 0.690
## (Adjusted p values reported -- fdr method)EN
GroupDiffEN <- aov(EN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffEN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.0846 0.028203 5.448 0.0011 **
## Alcohol 1 0.0000 0.000018 0.003 0.9533
## Marj 1 0.0115 0.011514 2.224 0.1366
## Age 1 0.0119 0.011913 2.301 0.1300
## Dataset 2 0.0224 0.011218 2.167 0.1158
## CPZ_eqiuv 1 0.0010 0.001027 0.198 0.6563
## Residuals 428 2.2157 0.005177
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessEN_group_plot <- effect_plot(GroupDiffEN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
EN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_EN <- glht(GroupDiffEN, linfct = mcp(Group="Tukey"))
summary(posthoc_EN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = EN ~ Group + Alcohol + Marj + Age + Dataset + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.0238427 0.0111941 -2.130 0.202
## ECP - HC == 0 -0.0115745 0.0119411 -0.969 0.609
## CP - HC == 0 -0.0003426 0.0148710 -0.023 0.982
## ECP - CHR == 0 0.0122682 0.0165140 0.743 0.609
## CP - CHR == 0 0.0235001 0.0185029 1.270 0.609
## CP - ECP == 0 0.0112319 0.0169286 0.663 0.609
## (Adjusted p values reported -- fdr method)MN
GroupDiffMN <- aov(MN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffMN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.0269 0.00898 1.645 0.178
## Alcohol 1 0.0001 0.00012 0.022 0.882
## Marj 1 0.0037 0.00371 0.680 0.410
## Age 1 0.0000 0.00001 0.002 0.966
## Dataset 2 0.1950 0.09749 17.864 3.54e-08 ***
## CPZ_eqiuv 1 0.0033 0.00329 0.603 0.438
## Residuals 427 2.3301 0.00546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11 observations deleted due to missingnessMN_group_plot <- effect_plot(GroupDiffMN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
MN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_MN <- glht(GroupDiffMN, linfct = mcp(Group="Tukey"))
summary(posthoc_MN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = MN ~ Group + Alcohol + Marj + Age + Dataset + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.005410 0.011526 -0.469 0.725
## ECP - HC == 0 0.004321 0.012260 0.352 0.725
## CP - HC == 0 -0.016202 0.015268 -1.061 0.725
## ECP - CHR == 0 0.009731 0.016978 0.573 0.725
## CP - CHR == 0 -0.010791 0.019018 -0.567 0.725
## CP - ECP == 0 -0.020523 0.017380 -1.181 0.725
## (Adjusted p values reported -- fdr method)AN
GroupDiffAN <- aov(AN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffAN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.0837 0.02791 4.427 0.004440 **
## Alcohol 1 0.0190 0.01902 3.016 0.083166 .
## Marj 1 0.0028 0.00280 0.445 0.505228
## Age 1 0.0259 0.02594 4.115 0.043132 *
## Dataset 2 0.1092 0.05459 8.658 0.000206 ***
## CPZ_eqiuv 1 0.0034 0.00336 0.534 0.465492
## Residuals 428 2.6987 0.00631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessAN_group_plot <- effect_plot(GroupDiffAN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
AN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_AN <- glht(GroupDiffAN, linfct = mcp(Group="Tukey"))
summary(posthoc_AN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = AN ~ Group + Alcohol + Marj + Age + Dataset + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.020031 0.012354 -1.621 0.211
## ECP - HC == 0 -0.025840 0.013178 -1.961 0.152
## CP - HC == 0 -0.038306 0.016412 -2.334 0.120
## ECP - CHR == 0 -0.005809 0.018225 -0.319 0.750
## CP - CHR == 0 -0.018275 0.020420 -0.895 0.557
## CP - ECP == 0 -0.012466 0.018683 -0.667 0.606
## (Adjusted p values reported -- fdr method)VN
GroupDiffVN <- aov(VN ~ Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, dataset)
summary(GroupDiffVN)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 0.0339 0.011294 3.336 0.0194 *
## Alcohol 1 0.0100 0.010023 2.960 0.0861 .
## Marj 1 0.0029 0.002917 0.862 0.3538
## Age 1 0.0095 0.009529 2.815 0.0941 .
## Dataset 2 0.0027 0.001357 0.401 0.6700
## CPZ_eqiuv 1 0.0058 0.005805 1.715 0.1911
## Residuals 428 1.4491 0.003386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 10 observations deleted due to missingnessVN_group_plot <- effect_plot(GroupDiffVN, pred = Group, interval = TRUE, partial.residuals = TRUE, jitter = .2)
VN_group_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1), axis.title.x = element_blank())
posthoc_VN <- glht(GroupDiffVN, linfct = mcp(Group="Tukey"))
summary(posthoc_VN, adjusted(type='fdr'))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = VN ~ Group + Alcohol + Marj + Age + Dataset + CPZ_eqiuv,
## data = dataset)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CHR - HC == 0 -0.002999 0.009053 -0.331 0.864
## ECP - HC == 0 -0.005282 0.009657 -0.547 0.864
## CP - HC == 0 -0.018934 0.012026 -1.574 0.638
## ECP - CHR == 0 -0.002283 0.013355 -0.171 0.864
## CP - CHR == 0 -0.015935 0.014963 -1.065 0.638
## CP - ECP == 0 -0.013653 0.013690 -0.997 0.638
## (Adjusted p values reported -- fdr method)CB-BG GE predicts Cort GE
FPN
FPN_4groups <- lm(FPN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(FPN_4groups)##
## Call:
## lm(formula = FPN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.300131 -0.047405 0.005178 0.052240 0.268794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.320e-01 1.686e-01 2.562 0.0108 *
## CCBN 3.379e-01 2.350e-01 1.438 0.1512
## MCBN 3.323e-01 2.608e-01 1.274 0.2033
## GroupCHR -6.662e-02 2.904e-01 -0.229 0.8187
## GroupECP -1.341e-01 3.687e-01 -0.364 0.7163
## GroupCP -3.046e-01 5.176e-01 -0.588 0.5566
## Alcohol -9.752e-03 7.536e-03 -1.294 0.1964
## Marj 1.231e-02 7.088e-03 1.737 0.0832 .
## Age -7.555e-04 5.757e-04 -1.312 0.1901
## DatasetCOBRE -1.213e-03 1.969e-02 -0.062 0.9509
## DatasetHCP -1.228e-02 2.032e-02 -0.604 0.5461
## CPZ_eqiuv -3.523e-06 7.464e-06 -0.472 0.6372
## CCBN:MCBN -3.727e-01 3.577e-01 -1.042 0.2981
## CCBN:GroupCHR 1.559e-01 4.255e-01 0.366 0.7143
## CCBN:GroupECP 1.495e-01 5.216e-01 0.287 0.7746
## CCBN:GroupCP 6.009e-01 7.325e-01 0.820 0.4125
## MCBN:GroupCHR -1.721e-02 4.493e-01 -0.038 0.9695
## MCBN:GroupECP 4.814e-01 5.776e-01 0.833 0.4051
## MCBN:GroupCP 5.229e-01 7.608e-01 0.687 0.4923
## CCBN:MCBN:GroupCHR -4.283e-02 6.394e-01 -0.067 0.9466
## CCBN:MCBN:GroupECP -6.279e-01 8.043e-01 -0.781 0.4354
## CCBN:MCBN:GroupCP -9.606e-01 1.067e+00 -0.901 0.3683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08304 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.0813, Adjusted R-squared: 0.03493
## F-statistic: 1.753 on 21 and 416 DF, p-value: 0.02146FPN_CCBN_plot <- effect_plot(FPN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
FPN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1))
FPN_MCBN_plot <- effect_plot(FPN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
FPN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
trellis.par.set(par.axis.text=list(cex=1.25))
trellis.par.set(par.strip.text=list(cex=2))
trellis.par.set(par.xlab.text=list(fontsize=0))
trellis.par.set(par.ylab.text=list(fontsize=0))
xyplot(FPN ~ CCBN | Group, data=dataset, fit = FPN_4groups, par.settings = list(strip.background=list(col="lightgrey"), par.strip.text = list(fontsize=20)),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(FPN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
CON
CON_4groups <- lm(CON ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(CON_4groups)##
## Call:
## lm(formula = CON ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45138 -0.08449 0.02508 0.09998 0.30597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.995e-01 3.016e-01 0.662 0.5086
## CCBN 9.312e-01 4.204e-01 2.215 0.0273 *
## MCBN 7.071e-01 4.666e-01 1.515 0.1304
## GroupCHR 3.645e-01 5.195e-01 0.702 0.4833
## GroupECP -1.901e-02 6.594e-01 -0.029 0.9770
## GroupCP 8.264e-02 9.259e-01 0.089 0.9289
## Alcohol -1.340e-02 1.348e-02 -0.994 0.3207
## Marj 8.232e-04 1.268e-02 0.065 0.9483
## Age -3.891e-04 1.030e-03 -0.378 0.7058
## DatasetCOBRE 2.025e-02 3.521e-02 0.575 0.5656
## DatasetHCP -1.785e-02 3.635e-02 -0.491 0.6236
## CPZ_eqiuv 2.141e-06 1.335e-05 0.160 0.8727
## CCBN:MCBN -1.188e+00 6.399e-01 -1.856 0.0641 .
## CCBN:GroupCHR -7.454e-01 7.612e-01 -0.979 0.3281
## CCBN:GroupECP -2.665e-01 9.330e-01 -0.286 0.7753
## CCBN:GroupCP -4.151e-02 1.310e+00 -0.032 0.9747
## MCBN:GroupCHR -7.482e-01 8.037e-01 -0.931 0.3524
## MCBN:GroupECP -5.053e-02 1.033e+00 -0.049 0.9610
## MCBN:GroupCP -2.224e-01 1.361e+00 -0.163 0.8702
## CCBN:MCBN:GroupCHR 1.290e+00 1.144e+00 1.128 0.2602
## CCBN:MCBN:GroupECP 4.505e-01 1.439e+00 0.313 0.7544
## CCBN:MCBN:GroupCP 2.139e-01 1.908e+00 0.112 0.9108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1485 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.07854, Adjusted R-squared: 0.03203
## F-statistic: 1.689 on 21 and 416 DF, p-value: 0.02972CON_CCBN_plot <- effect_plot(CON_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
CON_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
CON_MCBN_plot <- effect_plot(CON_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
CON_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(FPN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(FPN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
DMN
DMN_4groups <- lm(DMN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(DMN_4groups)##
## Call:
## lm(formula = DMN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.289668 -0.050216 0.008165 0.052942 0.272563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.399e-01 1.732e-01 3.695 0.000249 ***
## CCBN 1.569e-01 2.414e-01 0.650 0.515933
## MCBN 1.829e-01 2.679e-01 0.683 0.495112
## GroupCHR 1.290e-01 2.983e-01 0.432 0.665679
## GroupECP -8.584e-01 3.786e-01 -2.267 0.023883 *
## GroupCP 5.723e-01 5.316e-01 1.077 0.282283
## Alcohol 1.157e-03 7.740e-03 0.149 0.881267
## Marj 9.445e-03 7.279e-03 1.298 0.195170
## Age -5.021e-04 5.912e-04 -0.849 0.396212
## DatasetCOBRE -1.211e-02 2.022e-02 -0.599 0.549492
## DatasetHCP -2.898e-02 2.087e-02 -1.388 0.165764
## CPZ_eqiuv -5.693e-06 7.666e-06 -0.743 0.458156
## CCBN:MCBN -2.450e-01 3.674e-01 -0.667 0.505302
## CCBN:GroupCHR -1.841e-01 4.370e-01 -0.421 0.673734
## CCBN:GroupECP 1.124e+00 5.357e-01 2.099 0.036408 *
## CCBN:GroupCP -7.960e-01 7.523e-01 -1.058 0.290669
## MCBN:GroupCHR -1.332e-01 4.614e-01 -0.289 0.773012
## MCBN:GroupECP 1.124e+00 5.932e-01 1.895 0.058801 .
## MCBN:GroupCP -1.052e+00 7.814e-01 -1.347 0.178866
## CCBN:MCBN:GroupCHR 1.820e-01 6.566e-01 0.277 0.781732
## CCBN:MCBN:GroupECP -1.503e+00 8.261e-01 -1.819 0.069637 .
## CCBN:MCBN:GroupCP 1.451e+00 1.095e+00 1.325 0.185900
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08528 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.07715, Adjusted R-squared: 0.03056
## F-statistic: 1.656 on 21 and 416 DF, p-value: 0.03488DMN_CCBN_plot <- effect_plot(DMN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
DMN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
DMN_MCBN_plot <- effect_plot(DMN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
DMN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(DMN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(DMN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
EN
EN_4groups <- lm(EN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(EN_4groups)##
## Call:
## lm(formula = EN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33919 -0.03767 0.00706 0.04532 0.18161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.671e-01 1.442e-01 2.547 0.0112 *
## CCBN 3.347e-01 2.009e-01 1.666 0.0965 .
## MCBN 3.225e-01 2.230e-01 1.446 0.1488
## GroupCHR 2.233e-01 2.483e-01 0.899 0.3690
## GroupECP -7.594e-03 3.152e-01 -0.024 0.9808
## GroupCP 2.583e-01 4.426e-01 0.584 0.5598
## Alcohol -7.608e-04 6.443e-03 -0.118 0.9061
## Marj -9.596e-03 6.060e-03 -1.584 0.1140
## Age 4.443e-05 4.922e-04 0.090 0.9281
## DatasetCOBRE 2.714e-02 1.683e-02 1.612 0.1077
## DatasetHCP 2.161e-02 1.738e-02 1.244 0.2142
## CPZ_eqiuv -3.401e-06 6.382e-06 -0.533 0.5944
## CCBN:MCBN -3.703e-01 3.059e-01 -1.211 0.2267
## CCBN:GroupCHR -3.629e-01 3.638e-01 -0.998 0.3191
## CCBN:GroupECP -1.331e-02 4.459e-01 -0.030 0.9762
## CCBN:GroupCP -2.770e-01 6.263e-01 -0.442 0.6585
## MCBN:GroupCHR -4.460e-01 3.841e-01 -1.161 0.2462
## MCBN:GroupECP 2.060e-01 4.938e-01 0.417 0.6767
## MCBN:GroupCP -1.344e-01 6.505e-01 -0.207 0.8364
## CCBN:MCBN:GroupCHR 6.511e-01 5.466e-01 1.191 0.2343
## CCBN:MCBN:GroupECP -2.737e-01 6.877e-01 -0.398 0.6908
## CCBN:MCBN:GroupCP 7.156e-02 9.119e-01 0.078 0.9375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07099 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1067, Adjusted R-squared: 0.06165
## F-statistic: 2.367 on 21 and 416 DF, p-value: 0.0006845EN_CCBN_plot <- effect_plot(EN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
EN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
EN_MCBN_plot <- effect_plot(EN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
EN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(EN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(EN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
MN
MN_4groups <- lm(MN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(MN_4groups)##
## Call:
## lm(formula = MN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.296287 -0.044143 -0.000827 0.046852 0.182739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.855e-01 1.479e-01 4.635 4.79e-06 ***
## CCBN 1.172e-01 2.061e-01 0.568 0.5700
## MCBN 9.613e-02 2.288e-01 0.420 0.6746
## GroupCHR 5.484e-02 2.547e-01 0.215 0.8296
## GroupECP -7.252e-01 3.234e-01 -2.243 0.0254 *
## GroupCP -5.391e-01 4.540e-01 -1.187 0.2357
## Alcohol 3.649e-03 6.614e-03 0.552 0.5815
## Marj 4.119e-03 6.217e-03 0.663 0.5080
## Age -9.368e-04 5.050e-04 -1.855 0.0643 .
## DatasetCOBRE 1.887e-02 1.727e-02 1.093 0.2750
## DatasetHCP -4.774e-02 1.783e-02 -2.678 0.0077 **
## CPZ_eqiuv 1.638e-06 6.547e-06 0.250 0.8025
## CCBN:MCBN -6.828e-02 3.138e-01 -0.218 0.8279
## CCBN:GroupCHR -9.792e-02 3.732e-01 -0.262 0.7932
## CCBN:GroupECP 1.064e+00 4.575e-01 2.325 0.0206 *
## CCBN:GroupCP 6.518e-01 6.425e-01 1.014 0.3109
## MCBN:GroupCHR -1.016e-01 3.941e-01 -0.258 0.7966
## MCBN:GroupECP 1.257e+00 5.066e-01 2.482 0.0135 *
## MCBN:GroupCP 7.973e-01 6.673e-01 1.195 0.2328
## CCBN:MCBN:GroupCHR 1.655e-01 5.609e-01 0.295 0.7681
## CCBN:MCBN:GroupECP -1.809e+00 7.055e-01 -2.565 0.0107 *
## CCBN:MCBN:GroupCP -9.775e-01 9.355e-01 -1.045 0.2967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07283 on 415 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.1398, Adjusted R-squared: 0.09623
## F-statistic: 3.211 on 21 and 415 DF, p-value: 3.246e-06MN_CCBN_plot <- effect_plot(MN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
MN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
MN_MCBN_plot <- effect_plot(MN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
MN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(MN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(MN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
VN
VN_4groups <- lm(VN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(VN_4groups)##
## Call:
## lm(formula = VN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149975 -0.036045 -0.002834 0.035920 0.138783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.512e-01 1.174e-01 8.105 5.95e-15 ***
## CCBN -1.931e-01 1.636e-01 -1.181 0.23840
## MCBN -1.792e-01 1.815e-01 -0.987 0.32407
## GroupCHR -2.012e-01 2.021e-01 -0.995 0.32018
## GroupECP -8.079e-01 2.566e-01 -3.149 0.00176 **
## GroupCP -2.434e-01 3.603e-01 -0.676 0.49967
## Alcohol -1.445e-03 5.245e-03 -0.275 0.78308
## Marj -4.507e-03 4.933e-03 -0.914 0.36140
## Age -3.383e-04 4.007e-04 -0.844 0.39902
## DatasetCOBRE -9.058e-03 1.370e-02 -0.661 0.50891
## DatasetHCP 2.482e-03 1.415e-02 0.175 0.86080
## CPZ_eqiuv 5.277e-06 5.195e-06 1.016 0.31034
## CCBN:MCBN 2.715e-01 2.490e-01 1.091 0.27610
## CCBN:GroupCHR 3.835e-01 2.962e-01 1.295 0.19613
## CCBN:GroupECP 1.105e+00 3.630e-01 3.043 0.00249 **
## CCBN:GroupCP 3.538e-01 5.099e-01 0.694 0.48816
## MCBN:GroupCHR 3.084e-01 3.127e-01 0.986 0.32464
## MCBN:GroupECP 1.286e+00 4.020e-01 3.200 0.00148 **
## MCBN:GroupCP 3.427e-01 5.295e-01 0.647 0.51789
## CCBN:MCBN:GroupCHR -5.792e-01 4.450e-01 -1.302 0.19380
## CCBN:MCBN:GroupECP -1.757e+00 5.598e-01 -3.139 0.00182 **
## CCBN:MCBN:GroupCP -5.299e-01 7.423e-01 -0.714 0.47575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05779 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.08217, Adjusted R-squared: 0.03583
## F-statistic: 1.773 on 21 and 416 DF, p-value: 0.01933VN_CCBN_plot <- effect_plot(VN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
VN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
VN_MCBN_plot <- effect_plot(VN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
VN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(VN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(VN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
AN
AN_4groups <- lm(AN ~ CCBN*MCBN*Group + Alcohol+ Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(AN_4groups)##
## Call:
## lm(formula = AN ~ CCBN * MCBN * Group + Alcohol + Marj + Age +
## Dataset + CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.291869 -0.046590 0.003852 0.049693 0.190807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.772e-01 1.557e-01 2.422 0.01584 *
## CCBN 5.344e-01 2.170e-01 2.463 0.01420 *
## MCBN 5.020e-01 2.409e-01 2.084 0.03776 *
## GroupCHR 3.413e-01 2.682e-01 1.273 0.20390
## GroupECP -1.630e-01 3.404e-01 -0.479 0.63235
## GroupCP -1.624e-01 4.780e-01 -0.340 0.73428
## Alcohol 1.415e-02 6.959e-03 2.034 0.04258 *
## Marj 4.048e-03 6.545e-03 0.619 0.53654
## Age -1.473e-03 5.316e-04 -2.771 0.00585 **
## DatasetCOBRE 8.476e-03 1.818e-02 0.466 0.64124
## DatasetHCP -2.610e-02 1.877e-02 -1.391 0.16499
## CPZ_eqiuv 2.743e-06 6.893e-06 0.398 0.69085
## CCBN:MCBN -6.043e-01 3.303e-01 -1.829 0.06806 .
## CCBN:GroupCHR -5.442e-01 3.929e-01 -1.385 0.16680
## CCBN:GroupECP 1.470e-01 4.816e-01 0.305 0.76033
## CCBN:GroupCP 1.500e-01 6.764e-01 0.222 0.82457
## MCBN:GroupCHR -6.199e-01 4.149e-01 -1.494 0.13586
## MCBN:GroupECP 3.552e-01 5.333e-01 0.666 0.50583
## MCBN:GroupCP 1.769e-01 7.025e-01 0.252 0.80130
## CCBN:MCBN:GroupCHR 9.260e-01 5.904e-01 1.569 0.11752
## CCBN:MCBN:GroupECP -4.253e-01 7.427e-01 -0.573 0.56717
## CCBN:MCBN:GroupCP -1.911e-01 9.849e-01 -0.194 0.84628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07668 on 416 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1689, Adjusted R-squared: 0.1269
## F-statistic: 4.025 on 21 and 416 DF, p-value: 1.347e-08AN_CCBN_plot <- effect_plot(AN_4groups, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
AN_CCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
AN_MCBN_plot <- effect_plot(AN_4groups, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
AN_MCBN_plot + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
xyplot(AN ~ CCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
xyplot(AN ~ MCBN | Group, data=dataset, par.settings = list(strip.background=list(col="lightgrey")),
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col = "black")
panel.lmline(x, y, col = "black")
})
Combined table
export_summs(FPN_4groups, CON_4groups,DMN_4groups,EN_4groups,MN_4groups, VN_4groups,AN_4groups, to.file = "docx", file.name = "models.docx", model.names = c("FPN", "CON", "DMN", "EN", "MN", "VN", "AN"))| FPN | CON | DMN | EN | MN | VN | AN | |
|---|---|---|---|---|---|---|---|
| (Intercept) | 0.43 * | 0.20 | 0.64 *** | 0.37 * | 0.69 *** | 0.95 *** | 0.38 * |
| (0.17) | (0.30) | (0.17) | (0.14) | (0.15) | (0.12) | (0.16) | |
| CCBN | 0.34 | 0.93 * | 0.16 | 0.33 | 0.12 | -0.19 | 0.53 * |
| (0.24) | (0.42) | (0.24) | (0.20) | (0.21) | (0.16) | (0.22) | |
| MCBN | 0.33 | 0.71 | 0.18 | 0.32 | 0.10 | -0.18 | 0.50 * |
| (0.26) | (0.47) | (0.27) | (0.22) | (0.23) | (0.18) | (0.24) | |
| GroupCHR | -0.07 | 0.36 | 0.13 | 0.22 | 0.05 | -0.20 | 0.34 |
| (0.29) | (0.52) | (0.30) | (0.25) | (0.25) | (0.20) | (0.27) | |
| GroupECP | -0.13 | -0.02 | -0.86 * | -0.01 | -0.73 * | -0.81 ** | -0.16 |
| (0.37) | (0.66) | (0.38) | (0.32) | (0.32) | (0.26) | (0.34) | |
| GroupCP | -0.30 | 0.08 | 0.57 | 0.26 | -0.54 | -0.24 | -0.16 |
| (0.52) | (0.93) | (0.53) | (0.44) | (0.45) | (0.36) | (0.48) | |
| Alcohol | -0.01 | -0.01 | 0.00 | -0.00 | 0.00 | -0.00 | 0.01 * |
| (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
| Marj | 0.01 | 0.00 | 0.01 | -0.01 | 0.00 | -0.00 | 0.00 |
| (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.00) | (0.01) | |
| Age | -0.00 | -0.00 | -0.00 | 0.00 | -0.00 | -0.00 | -0.00 ** |
| (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | |
| DatasetCOBRE | -0.00 | 0.02 | -0.01 | 0.03 | 0.02 | -0.01 | 0.01 |
| (0.02) | (0.04) | (0.02) | (0.02) | (0.02) | (0.01) | (0.02) | |
| DatasetHCP | -0.01 | -0.02 | -0.03 | 0.02 | -0.05 ** | 0.00 | -0.03 |
| (0.02) | (0.04) | (0.02) | (0.02) | (0.02) | (0.01) | (0.02) | |
| CPZ_eqiuv | -0.00 | 0.00 | -0.00 | -0.00 | 0.00 | 0.00 | 0.00 |
| (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | |
| CCBN:MCBN | -0.37 | -1.19 | -0.24 | -0.37 | -0.07 | 0.27 | -0.60 |
| (0.36) | (0.64) | (0.37) | (0.31) | (0.31) | (0.25) | (0.33) | |
| CCBN:GroupCHR | 0.16 | -0.75 | -0.18 | -0.36 | -0.10 | 0.38 | -0.54 |
| (0.43) | (0.76) | (0.44) | (0.36) | (0.37) | (0.30) | (0.39) | |
| CCBN:GroupECP | 0.15 | -0.27 | 1.12 * | -0.01 | 1.06 * | 1.10 ** | 0.15 |
| (0.52) | (0.93) | (0.54) | (0.45) | (0.46) | (0.36) | (0.48) | |
| CCBN:GroupCP | 0.60 | -0.04 | -0.80 | -0.28 | 0.65 | 0.35 | 0.15 |
| (0.73) | (1.31) | (0.75) | (0.63) | (0.64) | (0.51) | (0.68) | |
| MCBN:GroupCHR | -0.02 | -0.75 | -0.13 | -0.45 | -0.10 | 0.31 | -0.62 |
| (0.45) | (0.80) | (0.46) | (0.38) | (0.39) | (0.31) | (0.41) | |
| MCBN:GroupECP | 0.48 | -0.05 | 1.12 | 0.21 | 1.26 * | 1.29 ** | 0.36 |
| (0.58) | (1.03) | (0.59) | (0.49) | (0.51) | (0.40) | (0.53) | |
| MCBN:GroupCP | 0.52 | -0.22 | -1.05 | -0.13 | 0.80 | 0.34 | 0.18 |
| (0.76) | (1.36) | (0.78) | (0.65) | (0.67) | (0.53) | (0.70) | |
| CCBN:MCBN:GroupCHR | -0.04 | 1.29 | 0.18 | 0.65 | 0.17 | -0.58 | 0.93 |
| (0.64) | (1.14) | (0.66) | (0.55) | (0.56) | (0.45) | (0.59) | |
| CCBN:MCBN:GroupECP | -0.63 | 0.45 | -1.50 | -0.27 | -1.81 * | -1.76 ** | -0.43 |
| (0.80) | (1.44) | (0.83) | (0.69) | (0.71) | (0.56) | (0.74) | |
| CCBN:MCBN:GroupCP | -0.96 | 0.21 | 1.45 | 0.07 | -0.98 | -0.53 | -0.19 |
| (1.07) | (1.91) | (1.10) | (0.91) | (0.94) | (0.74) | (0.98) | |
| N | 438 | 438 | 438 | 438 | 437 | 438 | 438 |
| R2 | 0.08 | 0.08 | 0.08 | 0.11 | 0.14 | 0.08 | 0.17 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | |||||||
Predicting Symptoms - Across All Groups (incl. HC)
PANSS-P
PANSS_P_all <- lm(PANSS.P ~ CCBN + MCBN + Alcohol + Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(PANSS_P_all)##
## Call:
## lm(formula = PANSS.P ~ CCBN + MCBN + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.849 -4.932 -1.623 4.542 21.686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.3082808 4.4261874 2.555 0.0112 *
## CCBN -3.1546793 4.9056537 -0.643 0.5207
## MCBN -3.9012181 2.8399059 -1.374 0.1707
## Alcohol 0.1178105 0.6466474 0.182 0.8556
## Marj 0.7670158 0.5643716 1.359 0.1753
## Age -0.0525154 0.0399089 -1.316 0.1894
## DatasetHCP 6.5877321 1.0742685 6.132 3.22e-09 ***
## CPZ_eqiuv 0.0037681 0.0004802 7.846 1.13e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.281 on 259 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.3689, Adjusted R-squared: 0.3518
## F-statistic: 21.62 on 7 and 259 DF, p-value: < 2.2e-16PANSS_P_CCBN_all_plot <- effect_plot(PANSS_P_all, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_P_CCBN_all_plot + ylab("PANSS-P") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1))
PANSS_P_MCBN_all_plot <- effect_plot(PANSS_P_all, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_P_MCBN_all_plot + ylab("PANSS-P") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
PANSS-N
PANSS_N_all <- lm(PANSS.N ~ CCBN + MCBN + Alcohol + Marj + Age + Dataset + CPZ_eqiuv, data = dataset)
summary(PANSS_N_all)##
## Call:
## lm(formula = PANSS.N ~ CCBN + MCBN + Alcohol + Marj + Age + Dataset +
## CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.455 -5.031 -2.076 4.332 17.997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.5561307 4.7776163 3.465 0.000620 ***
## CCBN -4.5110254 5.2951511 -0.852 0.395048
## MCBN -7.1161992 3.0653877 -2.321 0.021038 *
## Alcohol -0.6476066 0.6979896 -0.928 0.354366
## Marj 0.6775389 0.6091814 1.112 0.267078
## Age -0.0734601 0.0430776 -1.705 0.089338 .
## DatasetHCP 4.4557512 1.1595629 3.843 0.000153 ***
## CPZ_eqiuv 0.0036446 0.0005184 7.031 1.82e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.78 on 259 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.2922, Adjusted R-squared: 0.2731
## F-statistic: 15.27 on 7 and 259 DF, p-value: < 2.2e-16PANSS_N_CCBN_all_plot <- effect_plot(PANSS_N_all, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_N_CCBN_all_plot + ylab("PANSS-N") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
PANSS_N_MCBN_all_plot <- effect_plot(PANSS_N_all, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_N_MCBN_all_plot + ylab("PANSS-N") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
PANSS-Cog
PANSS_C_all <- lm(PANSS.Cog ~ CCBN + MCBN + Alcohol + Marj + Age + CPZ_eqiuv, data = dataset)
summary(PANSS_C_all)##
## Call:
## lm(formula = PANSS.Cog ~ CCBN + MCBN + Alcohol + Marj + Age +
## CPZ_eqiuv, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6882 -1.2537 -0.2705 1.3625 6.0455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.027497 2.845298 1.767 0.08009 .
## CCBN 9.169216 3.131570 2.928 0.00417 **
## MCBN -2.307249 1.967310 -1.173 0.24348
## Alcohol -0.089567 0.399696 -0.224 0.82312
## Marj 0.019725 0.296514 0.067 0.94709
## Age 0.008272 0.072483 0.114 0.90935
## CPZ_eqiuv 0.004868 0.001187 4.102 8.01e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.711 on 107 degrees of freedom
## (334 observations deleted due to missingness)
## Multiple R-squared: 0.2373, Adjusted R-squared: 0.1945
## F-statistic: 5.548 on 6 and 107 DF, p-value: 4.871e-05PANSS_Cog_CCBN_all_plot <- effect_plot(PANSS_C_all, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_Cog_CCBN_all_plot + ylab("PANSS-Cog") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
PANSS_Cog_MCBN_all_plot <- effect_plot(PANSS_C_all, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
PANSS_Cog_MCBN_all_plot + ylab("PANSS-Cog") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
SIPS-P
SIPS_P_all <- lm(SIPS.P ~ CCBN + MCBN + Alcohol + Marj + Age, data = dataset)
summary(SIPS_P_all)##
## Call:
## lm(formula = SIPS.P ~ CCBN + MCBN + Alcohol + Marj + Age, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.427 -4.712 -1.031 3.954 17.805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.5842 5.5714 -0.284 0.7765
## CCBN -6.3237 4.5532 -1.389 0.1668
## MCBN 3.1305 3.0702 1.020 0.3094
## Alcohol 4.2321 1.0379 4.078 7.06e-05 ***
## Marj 7.4725 1.7723 4.216 4.08e-05 ***
## Age 0.4717 0.1948 2.422 0.0165 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.817 on 165 degrees of freedom
## (277 observations deleted due to missingness)
## Multiple R-squared: 0.2487, Adjusted R-squared: 0.226
## F-statistic: 10.93 on 5 and 165 DF, p-value: 4.273e-09SIPS_P_CCBN_all_plot <- effect_plot(SIPS_P_all, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
SIPS_P_CCBN_all_plot + ylab("SIPS-P") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
SIPS_P_MCBN_all_plot <- effect_plot(SIPS_P_all, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
SIPS_P_MCBN_all_plot + ylab("SIPS-P") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
SIPS-N
SIPS_N_all <- lm(SIPS.N ~ CCBN + MCBN + Alcohol + Marj + Age, data = dataset)
summary(SIPS_N_all)##
## Call:
## lm(formula = SIPS.N ~ CCBN + MCBN + Alcohol + Marj + Age, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.322 -3.336 -1.926 2.181 19.398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2772 5.2963 -0.052 0.958316
## CCBN -5.2717 4.3284 -1.218 0.224982
## MCBN 4.8363 2.9186 1.657 0.099411 .
## Alcohol 3.7114 0.9866 3.762 0.000234 ***
## Marj 7.3051 1.6848 4.336 2.52e-05 ***
## Age 0.2067 0.1852 1.116 0.265949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.53 on 165 degrees of freedom
## (277 observations deleted due to missingness)
## Multiple R-squared: 0.2113, Adjusted R-squared: 0.1874
## F-statistic: 8.843 on 5 and 165 DF, p-value: 1.866e-07SIPS_N_CCBN_all_plot <- effect_plot(SIPS_N_all, pred = CCBN, interval = TRUE, partial.residuals = TRUE)
SIPS_N_CCBN_all_plot + ylab("SIPS-N") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
SIPS_N_MCBN_all_plot <- effect_plot(SIPS_N_all, pred = MCBN, interval = TRUE, partial.residuals = TRUE)
SIPS_N_MCBN_all_plot + ylab("SIPS-N") + theme(text = element_text(size = 24), panel.border = element_rect(colour = "black", fill=NA, size=1)) 
Combined table
export_summs(PANSS_P_all, PANSS_N_all, PANSS_C_all, SIPS_P_all, SIPS_N_all, to.file = "docx", file.name = "models_symptoms.docx", model.names = c("PANSS-P", "PANSS-N", "PANSS-C", "SIPS-P", "SIPS-N"))| PANSS-P | PANSS-N | PANSS-C | SIPS-P | SIPS-N | |
|---|---|---|---|---|---|
| (Intercept) | 11.31 * | 16.56 *** | 5.03 | -1.58 | -0.28 |
| (4.43) | (4.78) | (2.85) | (5.57) | (5.30) | |
| CCBN | -3.15 | -4.51 | 9.17 ** | -6.32 | -5.27 |
| (4.91) | (5.30) | (3.13) | (4.55) | (4.33) | |
| MCBN | -3.90 | -7.12 * | -2.31 | 3.13 | 4.84 |
| (2.84) | (3.07) | (1.97) | (3.07) | (2.92) | |
| Alcohol | 0.12 | -0.65 | -0.09 | 4.23 *** | 3.71 *** |
| (0.65) | (0.70) | (0.40) | (1.04) | (0.99) | |
| Marj | 0.77 | 0.68 | 0.02 | 7.47 *** | 7.31 *** |
| (0.56) | (0.61) | (0.30) | (1.77) | (1.68) | |
| Age | -0.05 | -0.07 | 0.01 | 0.47 * | 0.21 |
| (0.04) | (0.04) | (0.07) | (0.19) | (0.19) | |
| DatasetHCP | 6.59 *** | 4.46 *** | |||
| (1.07) | (1.16) | ||||
| CPZ_eqiuv | 0.00 *** | 0.00 *** | 0.00 *** | ||
| (0.00) | (0.00) | (0.00) | |||
| N | 267 | 267 | 114 | 171 | 171 |
| R2 | 0.37 | 0.29 | 0.24 | 0.25 | 0.21 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | |||||